A Neo4j Implementation of Fuzzy Random Walkers

Drakopoulos, Georgios; Kanavos, Andreas; Tsakalidis, Athanasios K.

doi:10.1145/2903220.2903256

Cited by 4 publications

(6 citation statements)

References 11 publications

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“…Additionally, Chebyshev Walktrap relies on the competitive factors of second order statistics though the Chebyshev inequality and on an optional relocation capability in order to bound unnecessarily costly walks and, thus, remaining inside a community and being trapped for too long within the boundaries of a community, respectively. The relocation aspect was also backported to the Markov Walktrap algorithm first proposed in [15]. The effect of relocation on the community coherence was evaluated based on the asymmetric Tversky index using the Fuzzy Newman-Girvan algorithm from [15] as baseline, while its effect on the output distribution was assessed with the asymmetric Kullback-Leibler divergence.…”

Section: Discussionmentioning

confidence: 99%

“…The relocation aspect was also backported to the Markov Walktrap algorithm first proposed in [15]. The effect of relocation on the community coherence was evaluated based on the asymmetric Tversky index using the Fuzzy Newman-Girvan algorithm from [15] as baseline, while its effect on the output distribution was assessed with the asymmetric Kullback-Leibler divergence. The latter was also the basis for evaluating the distance between the community size distribution generated by Fuzzy Newman-Girvan and the one computed by the Makrov Walktrap and the Chebyshev Walktrap.…”

Section: Discussionmentioning

confidence: 99%

“…To this end, the Random Walker has the option as in [15] to reverse its strategy and select neighboring vertices with probability inversely proportional to their probability of existence if a random flag is triggered. The latter was implemented as a Bernoulli random variable with success probability q 0 , which can be set to zero if so desired in order to disable the weight inversion strategy.…”

Section: Escape Strategiesmentioning

confidence: 99%

“…Recommended values are typically O |V| − , ≥ 2. Therefore, as in [15], when weight inversion is enabled, the distance d between communities implicitly depends on terms of the form…”

Section: Escape Strategiesmentioning

confidence: 99%

“…The last two algorithms are outlined in [10], whereas Fuzzy Markov was proposed in [15]. The effect of the escape mechanisms of weight inversion (I) and relocation (R) are also shown for Markov Walktrap and Chebyshev Walktrap.…”

Section: Time and Memory Requirementsmentioning

confidence: 99%

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Fuzzy Random Walkers with Second Order Bounds: An Asymmetric Analysis

2017

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Edge-fuzzy graphs constitute an essential modeling paradigm across a broad spectrum of domains ranging from artificial intelligence to computational neuroscience and social network analysis. Under this model, fundamental graph properties such as edge length and graph diameter become stochastic and as such they are consequently expressed in probabilistic terms. Thus, algorithms for fuzzy graph analysis must rely on non-deterministic design principles. One such principle is Random Walker, which is based on a virtual entity and selects either edges or, like in this case, vertices of a fuzzy graph to visit. This allows the estimation of global graph properties through a long sequence of local decisions, making it a viable strategy candidate for graph processing software relying on native graph databases such as Neo4j. As a concrete example, Chebyshev Walktrap, a heuristic fuzzy community discovery algorithm relying on second order statistics and on the teleportation of the Random Walker, is proposed and its performance, expressed in terms of community coherence and number of vertex visits, is compared to the previously proposed algorithms of Markov Walktrap, Fuzzy Walktrap, and Fuzzy Newman-Girvan. In order to facilitate this comparison, a metric based on the asymmetric metrics of Tversky index and Kullback-Leibler divergence is used.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Escape Strategiesmentioning

confidence: 99%

“…Recommended values are typically O |V| − , ≥ 2. Therefore, as in [15], when weight inversion is enabled, the distance d between communities implicitly depends on terms of the form…”

Section: Escape Strategiesmentioning

confidence: 99%

Section: Time and Memory Requirementsmentioning

confidence: 99%

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Fuzzy Random Walkers with Second Order Bounds: An Asymmetric Analysis

2017

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The Biomolecular Computation Paradigm: A Survey in Massive Biological Computation

Drakopoulos¹,

Tsolis²,

Stefani³

et al. 2018

IFIP Advances in Information and Communication Technology

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Biomolecular computation is the scientific field focusing on the theory and practice of encoding combinatorial problems in ordinary DNA strands and applying standard biology lab operations such as cleansing and complementary sequence generation to them in order to compute an exact solution. The primary advantage offered by this computational paradigm is massive parallelism as the solution space is simultaneously searched. On the other hand, factors that need to addressed under this model are the DNA volume growth and computational errors attributed to inexact DNA matching. Biomolecular computation additionally paves the way for two-and three-dimensional self assemblying biological tiles which are closely linked at a theoretical level to a Turing machine, establishing thus its computational power. Applications include medium sized instances of TSP and the evaluation of the output of bounded fan-out Boolean circuits.

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